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PhD Research Fellow at the interface between statistics, logic and machine learning
Universitetet i Oslo
Boks 1072 Blindern, 0316 OSLO
To måneder siden
kr 258 - 277
Per time
kr 44 683 - 47 950
Per måned
kr 536 200 - 575 400
Per år
Oppsummert av KI
Rapporter feilOm stillingen
Integreat – Norwegian Centre for Knowledge-driven Machine Learning is seeking a motivated PhD candidate in machine learning, knowledge representation, logic or statistics, to join our interdisciplinary research centre at the University of Oslo, Norway.
This is a unique opportunity to contribute to cutting-edge research at the intersection of machine learning, statistics, and logic—within a collaborative and supportive academic environment.
You will be part of a dynamic group of early career researchers, supervised by senior experts, collaborating across disciplines to tackle fundamental challenges through innovative methods, theory and critical analysis.
The fellowship period is 3 years.
Starting date as soon as possible and upon individual agreement.
An extension of the appointment by up to twelve months may be considered, which will be devoted to career enhancing compulsory work duties, e.g. teaching or supervision. This will be dependent on the qualifications of the applicant and the specific teaching need of the employment department.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
Integreat – Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway (2023-33). Integreat has two branches, one in Oslo (University of Oslo, UiO) and one in Tromsø (UiT The Arctic University of Norway).
Machine learning is the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data, laying the foundations of next generation machine learning. We do this by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technology, ethics and machine learning, in new and unique ways.
Focus of Integreat is to develop groundbreaking methods and theories, and therefore solving fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Centre (NR) and the ML group at UiT, with members from the Departments of Physics and Technology, Mathematics and Statistics, and Computer Science.
This project links together knowledge graphs with uncertainty quantification in situations where domain knowledge can be exploited. Each of these research areas is vibrant and important on its own right, and this project aims to bring them together in a meaningful way. One of the most interesting frameworks for quantifying the uncertainty of predictions is conformal prediction (CP). Under appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods for knowledge graphs (KGs), which are one of the most popular approaches for (semi-)structured data. There are many learning tasks on KGs, such as KG completion, link prediction, and node and graph classification. Graph Neural Networks (GNNs) are very successful for learning on KGs and solving the mentioned tasks, but also have great potential for incorporating symbolic knowledge due to strong connections between GNNs and logics. Such knowledge can be represented in common practical languages based on First Order Logic, as well as its fragments and extensions. We will develop methods for logic-aware CP on KGs using GNN for prediction and design new algorithms with theoretical guarantees. This PhD project will be at the interface between statistics, logic and machine learning.
Project supervisors: Egor V. Kostylev, Arnoldo Frigessi
Working language: English.
This position is at the University of Oslo, Integreat – Norwegian Centre for Knowledge-driven Machine Learning, and the Department of Informatics, with the place of work at Integreat.
The centre values inclusive excellence and is committed to fostering an environment where all voices are heard and respected.
We offer excellent opportunities for mentorship, international collaboration, and academic growth. If you are passionate about impactful research, eager to learn, or looking to grow in a team-oriented culture—we encourage you to apply and bring your perspective to our community.
The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.
Qualification requirements:
All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions regulation, and the national security regulation.
Personal skills:
We are looking for candidates who are curious, open-minded, and motivated to learn. You should enjoy working both independently and as part of a team, and be comfortable communicating your ideas clearly across disciplines. A collaborative spirit, a strong sense of responsibility, and a willingness to contribute to an inclusive and respectful research culture are essential.
Language requirement:
Grade requirements:
The norm is as follows:
Candidates without a master’s degree have until 01.09.2025 to complete the final exam.
The purpose of the fellowship is research training leading to the successful completion of a PhD degree. For more information see:
http://www.mn.uio.no/english/research/phd/
Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.
If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.
We hope that you will apply for the position.
More information about gender equality initiatives at UiO can be found here.
Your application should include:
Application with attachments must be submitted via our recruitment system Jobbnorge, click "Apply for the position".
When applying for the position, we ask you to retrieve your education results from Vitnemålsportalen.no. If your education results are not available through Vitnemålsportalen, we ask you to upload copies of your transcripts or grades. Please note that all documentation must be in English or a Scandinavian language.
The best qualified candidates will invited for interviews.
Applicant lists can be published in accordance with Norwegian Freedom of Information Act § 25. When you apply for a position with us, your name will appear on the public applicant list. It is possible to request to be excluded from this list. You must justify why you want an exemption from publication and we will then decide whether we can grant your request. If we can't, you will hear from us.
Please refer to Regulations for the Act on universities and colleges chapter 3 (Norwegian), Guidelines concerning appointment to post doctoral and research posts at UiO (Norwegian) and Regulations for the degree of Philosophiae Doctor (PhD) at the University of Oslo.
The University of Oslo has a transfer agreement with all employees that is intended to secure the rights to all research results etc.
Om bedriften
Universitetet i Oslo er Norges eldste og høyest rangerte forsknings- og utdanningsinstitusjon med 26 500 studenter og 7 200 ansatte. Faglig bredde og internasjonalt anerkjente forskningsmiljøer gjør UiO til en viktig samfunnsaktør.
Institutt for klinisk medisin har sin primærvirksomhet i krysningspunktet mellom universitets- og sykehussektoren, og samarbeider tett med universitetssykehusene i Oslo (OUS) og Akershus (Ahus). Instituttet er ledende innen klinisk medisin og medisinsk forskning i Norge, og har høy undervisnings- og forskningsaktivitet. Arbeidsfeltet er bredt, og instituttet preges av solid faglig kompetanse, mangfoldighet og kompleksitet. Institutt for klinisk medisin er UiOs største institutt med i underkant av 900 medarbeidere. Instituttet huser tre Sentre for fremragende forskning (SFF) og fem K. G. Jebsensentre. Instituttet er underlagt Det medisinske fakultet ved Universitetet i Oslo.
Tittel
PhD Research Fellow at the interface between statistics, logic and machine learning
Oppstart
Type engasjement
Vikariat
Sektor
Offentlig
Omfang
Heltid
Antall stillinger
1
Del annonsen
-icon.svg){kind=icon}
Om stillingen
Integreat – Norwegian Centre for Knowledge-driven Machine Learning is seeking a motivated PhD candidate in machine learning, knowledge representation, logic or statistics, to join our interdisciplinary research centre at the University of Oslo, Norway.
This is a unique opportunity to contribute to cutting-edge research at the intersection of machine learning, statistics, and logic—within a collaborative and supportive academic environment.
You will be part of a dynamic group of early career researchers, supervised by senior experts, collaborating across disciplines to tackle fundamental challenges through innovative methods, theory and critical analysis.
The fellowship period is 3 years.
Starting date as soon as possible and upon individual agreement.
An extension of the appointment by up to twelve months may be considered, which will be devoted to career enhancing compulsory work duties, e.g. teaching or supervision. This will be dependent on the qualifications of the applicant and the specific teaching need of the employment department.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
Integreat – Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway (2023-33). Integreat has two branches, one in Oslo (University of Oslo, UiO) and one in Tromsø (UiT The Arctic University of Norway).
Machine learning is the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data, laying the foundations of next generation machine learning. We do this by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technology, ethics and machine learning, in new and unique ways.
Focus of Integreat is to develop groundbreaking methods and theories, and therefore solving fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Centre (NR) and the ML group at UiT, with members from the Departments of Physics and Technology, Mathematics and Statistics, and Computer Science.
This project links together knowledge graphs with uncertainty quantification in situations where domain knowledge can be exploited. Each of these research areas is vibrant and important on its own right, and this project aims to bring them together in a meaningful way. One of the most interesting frameworks for quantifying the uncertainty of predictions is conformal prediction (CP). Under appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods for knowledge graphs (KGs), which are one of the most popular approaches for (semi-)structured data. There are many learning tasks on KGs, such as KG completion, link prediction, and node and graph classification. Graph Neural Networks (GNNs) are very successful for learning on KGs and solving the mentioned tasks, but also have great potential for incorporating symbolic knowledge due to strong connections between GNNs and logics. Such knowledge can be represented in common practical languages based on First Order Logic, as well as its fragments and extensions. We will develop methods for logic-aware CP on KGs using GNN for prediction and design new algorithms with theoretical guarantees. This PhD project will be at the interface between statistics, logic and machine learning.
Project supervisors: Egor V. Kostylev, Arnoldo Frigessi
Working language: English.
This position is at the University of Oslo, Integreat – Norwegian Centre for Knowledge-driven Machine Learning, and the Department of Informatics, with the place of work at Integreat.
The centre values inclusive excellence and is committed to fostering an environment where all voices are heard and respected.
We offer excellent opportunities for mentorship, international collaboration, and academic growth. If you are passionate about impactful research, eager to learn, or looking to grow in a team-oriented culture—we encourage you to apply and bring your perspective to our community.
The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.
Qualification requirements:
All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions regulation, and the national security regulation.
Personal skills:
We are looking for candidates who are curious, open-minded, and motivated to learn. You should enjoy working both independently and as part of a team, and be comfortable communicating your ideas clearly across disciplines. A collaborative spirit, a strong sense of responsibility, and a willingness to contribute to an inclusive and respectful research culture are essential.
Language requirement:
Grade requirements:
The norm is as follows:
Candidates without a master’s degree have until 01.09.2025 to complete the final exam.
The purpose of the fellowship is research training leading to the successful completion of a PhD degree. For more information see:
http://www.mn.uio.no/english/research/phd/
Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.
If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.
We hope that you will apply for the position.
More information about gender equality initiatives at UiO can be found here.
Your application should include:
Application with attachments must be submitted via our recruitment system Jobbnorge, click "Apply for the position".
When applying for the position, we ask you to retrieve your education results from Vitnemålsportalen.no. If your education results are not available through Vitnemålsportalen, we ask you to upload copies of your transcripts or grades. Please note that all documentation must be in English or a Scandinavian language.
The best qualified candidates will invited for interviews.
Applicant lists can be published in accordance with Norwegian Freedom of Information Act § 25. When you apply for a position with us, your name will appear on the public applicant list. It is possible to request to be excluded from this list. You must justify why you want an exemption from publication and we will then decide whether we can grant your request. If we can't, you will hear from us.
Please refer to Regulations for the Act on universities and colleges chapter 3 (Norwegian), Guidelines concerning appointment to post doctoral and research posts at UiO (Norwegian) and Regulations for the degree of Philosophiae Doctor (PhD) at the University of Oslo.
The University of Oslo has a transfer agreement with all employees that is intended to secure the rights to all research results etc.
Om bedriften
Universitetet i Oslo er Norges eldste og høyest rangerte forsknings- og utdanningsinstitusjon med 26 500 studenter og 7 200 ansatte. Faglig bredde og internasjonalt anerkjente forskningsmiljøer gjør UiO til en viktig samfunnsaktør.
Institutt for klinisk medisin har sin primærvirksomhet i krysningspunktet mellom universitets- og sykehussektoren, og samarbeider tett med universitetssykehusene i Oslo (OUS) og Akershus (Ahus). Instituttet er ledende innen klinisk medisin og medisinsk forskning i Norge, og har høy undervisnings- og forskningsaktivitet. Arbeidsfeltet er bredt, og instituttet preges av solid faglig kompetanse, mangfoldighet og kompleksitet. Institutt for klinisk medisin er UiOs største institutt med i underkant av 900 medarbeidere. Instituttet huser tre Sentre for fremragende forskning (SFF) og fem K. G. Jebsensentre. Instituttet er underlagt Det medisinske fakultet ved Universitetet i Oslo.
kr 258 - 277
Per time
kr 44 683 - 47 950
Per måned
kr 536 200 - 575 400
Per år
Oppsummert av KI
Rapporter feilTittel
PhD Research Fellow at the interface between statistics, logic and machine learning
Oppstart
Type engasjement
Vikariat
Sektor
Offentlig
Omfang
Heltid
Antall stillinger
1
Del annonsen
Relaterte stillinger
PhD Research Fellow at the interface between statistics, logic and machine learning
Universitetet i Oslo
OSLO
To måneder siden